Medical data processing method, cluster processing system and method thereof

ABSTRACT

A medical data cluster processing system, including: a cluster initiator device and cluster participant devices that have signal connection with each other. The cluster initiator device sends model data and medical data to at least two cluster participant devices; the cluster participant devices receive the model data and the medical data from the cluster initiator device, process the medical data based on the model data to obtain second processing data, and sends the second processing data to the cluster initiator device; and the cluster initiator device receives the second processing data sent from the at least two cluster participant devices, and comprehensively processes the second processing data to obtain a medical data processing result. A medical data processing method and a medical data cluster processing method are also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is the National Stage of PCT/CN2017/085726 filed on May24, 2017, which claims priority under 35 U.S.C. § 119 of ChineseApplication No. 201610810601.8 filed on Sep. 8, 2016, the disclosure ofwhich is incorporated by reference.

TECHNICAL FIELD

Embodiments of the present disclosure relate to a medical dataprocessing method, a medical data cluster processing system, and amedical data cluster processing method.

BACKGROUND

Currently, processing of medical data mainly depends on somehigh-performance devices with specific purposes, such as a high-enddevice with high computing power. However, such a high-end device is notusually equipped in general hospitals. For grassroots medical clinics,or in some remote areas, a medical device with high computingperformance is often unavailable. Moreover, these high-end devices areoften disposed in fixed positions, and are difficult for the generalpatients to access.

In addition, with the development of data processing technology, moreeffective medical data algorithms continue to emerge. However, thesealgorithms require higher computation amount, and for a home portablemedical device, it is difficult to run these algorithms on the homeportable medical device. Currently, in order to calculate a large amountof complex medical data, one approach is to upload the data to a medicaldata center through the network for processing, and then download aresult to a local end. But this approach may lead to a problem ofprivacy leakage of patient data. Another approach is to enhance theprocessing power of the local hardware. However, this approach isunrealistic for the grassroots medical clinics or in some remote areas.For a general medical device, enhancement of the local computing powerwill greatly increase the cost of the device, and frequency of using theenhanced local computing power in the whole life cycle of the device isnot high, which will cause the waste of resources.

SUMMARY

A purpose of an embodiment of the present disclosure is to provide amedical data cluster processing system, a medical data clusterprocessing method, and a medical data processing method, so as to solvetechnical problems mentioned above.

According to at least an embodiment of the present disclosure, a medicaldata cluster processing system is provided. The medical data clusterprocessing system comprises: a cluster initiator device and clusterparticipant devices having signal connection with the cluster initiator.The cluster initiator device sends model data and medical data to atleast two cluster participant devices; the cluster participant devicesreceive the model data and the medical data from the cluster initiatordevice, process the medical data based on the model data to obtainsecond processing data, and send the second processing data to thecluster initiator device; and the cluster initiator device receives thesecond processing data sent from the at least two cluster participantdevices, and comprehensively processes the second processing data toobtain a medical data processing result.

For example, the cluster participant devices each comprise a safetyprocessing virtual area, the safety processing virtual area is safelyisolated from other areas of the cluster participant devices; and thecluster participant devices process the medical data based on the modeldata in the safety processing virtual area, so as to obtain the secondprocessing data.

For example, the cluster initiator device accesses the safety processingvirtual area of each cluster participant device and controls theprocessing in the safety processing virtual area, and the clusterparticipant devices do not have access to the safety processing virtualarea.

According to at least an embodiment of the present disclosure, a medicaldata cluster processing method is provided, with a cluster comprising acluster initiator device and at least two cluster participant devices,and the cluster initiator device and the at least two clusterparticipant devices having signal connection with each other. Themedical data cluster processing method comprises: sending, by thecluster initiator device, model data and medical data to the at leasttwo cluster participant devices respectively; receiving, by the clusterparticipant devices, the model data and the medical data from thecluster initiator device; processing, by the cluster participantdevices, the medical data based on the model data received to obtainsecond processing data, and sending the second processing data to thecluster initiator device; and receiving, by the cluster initiatordevice, the second processing data sent from the at least two clusterparticipant devices, and comprehensively processing the secondprocessing data to obtain a medical data processing result.

For example, the cluster initiator device divides the model data intomultiple pieces of first sub data; the cluster initiator device sends afirst sub data set including the multiple pieces of first sub data as awhole to each of the at least two cluster participant devicesrespectively; each cluster participant device receives the first subdata set from the cluster initiator device; and each cluster participantdevice processes the medical data based on the first sub data setreceived to obtain a corresponding piece of the second processing data,and sends the corresponding piece of the second processing data to thecluster initiator device.

For example, the cluster initiator device divides the model data intomultiple pieces of first sub data, and divides the multiple pieces offirst sub data into multiple groups; the cluster initiator device sendsone or more groups of the first sub data to one of the at least twocluster participant devices, and sends other groups of the first subdata to others of the at least two cluster participant devices; eachcluster participant device receives one or more groups of the first subdata from the cluster initiator device; and each cluster participantdevice processes the medical data based on the one or more groups of thefirst sub data received to obtain a corresponding piece of the secondprocessing data, and sends the corresponding piece of the secondprocessing data to the cluster initiator device.

For example, the medical data cluster processing method furthercomprises: dividing, by the cluster initiator device, the medical datainto multiple pieces of second sub data; dividing, by the clusterinitiator device, the multiple pieces of second sub data into at leasttwo groups, assigning one or more groups of the second sub data to asecond electronic device and assigning remaining groups of the secondsub data to other second electronic devices; and processing, by eachcluster participant device, one or more groups of the second sub datareceived based on the model data to obtain a corresponding piece of thesecond processing data, and sending corresponding piece of the secondprocessing data to the cluster initiator device.

According to at least an embodiment of the disclosure, a medical dataprocessing method, applied in a first electronic device, is provided.The medical data processing method comprises: sending model data andmedical data to at least two second electronic devices, the secondelectronic devices being connected with the first electronic device;receiving multiple pieces of second processing data sent from the atleast two second electronic devices, wherein the multiple pieces ofsecond processing data are processing data obtained by processing themedical data based on the model data by the second electronic devices;and processing the multiple pieces of second processing data receivedfrom the at least two second electronic devices to obtain a medical dataprocessing result.

For example, the model data comprises a first sub data set includingmultiple pieces of first sub data; and the first sub data set as a wholeis sent to each of the at least two second electronic devices.

For example, the model data comprises a first sub data set includingmultiple pieces of first sub data; the multiple pieces of first sub datain the first sub data set are grouped to obtain a plurality of first subdata groups; at least one of the first sub data groups is sent to one ofthe at least two second electronic devices; and other first sub datagroups are sent to others of the at least two second electronic devices.

For example, the medical data processing method comprises: obtainingprocessing performance parameters of the at least two second electronicdevices; and sending the first sub data groups to the at least twosecond electronic devices based on the processing performance parametersof the at least two second electronic devices.

For example, at least two pieces of first sub data in the first sub dataset are related to each other, and the at least two pieces of first subdata related to each other are sent to one second electronic device.

For example, at least two pieces of first sub data in the first sub dataset are related to each other, and the at least two pieces of first subdata related to each other are respectively sent to different secondelectronic devices.

For example, the medical data processing method further comprises:receiving the second processing data sent from the at least two secondelectronic devices respectively; and correcting the second processingdata to obtain third data.

For example, the medical data processing method comprises: determiningan initial weight of each piece of first sub data; when a piece of firstsub data in the first sub data set is sent to a second electronicdevice, sending an initial weight of the piece of first sub data to thesecond electronic device simultaneously; and based on initial weights ofthe multiple pieces of first sub data, processing the multiple pieces ofsecond processing data received from the at least two second electronicdevices to obtain third data.

For example, the medical data processing method comprises: receivingfirst processing data sent by the at least two second electronicdevices, wherein the first processing data includes results obtainedafter the second electronic devices process the model data; and based onthe first processing data, processing the multiple pieces of secondprocessing data sent by the at least two second electronic devices toobtain third data.

For example, the model data comprises multiple pieces of first sub data,the first processing data comprises second weights of the multiplepieces of first sub data, the second weights are weights afterprocessing the initial weights by the second electronic devices; theinitial weights are the initial weights of the multiple pieces of firstsub data sent to the second electronic devices; and the secondprocessing data is processed based on the second weights of the multiplepieces of first sub data to obtain the third data.

For example, the medical data comprises a second sub data set includingmultiple pieces of second sub data; the multiple pieces of second subdata in the second sub data set are divided into at least two groups toobtain a plurality of second sub data groups; at least one of the secondsub data groups is sent to one of the at least two second electronicdevices; and other second sub data groups are sent to others of the atleast two second electronic devices.

For example, the medical data processing method further comprises: afterencrypting the model data and the medical data, sending the model dataand the medical data to at least two second electronic devices, andreceiving the encrypted second processing data sent by the at least twosecond electronic devices.

According to at least an embodiment of the disclosure, a medical dataprocessing method, applied in at least two second electronic devices,comprising: receiving model data and medical data from a firstelectronic device, the second electronic devices being connected withthe first electronic device; based on the model data, processing themedical data to obtain second processing data by the at least two secondelectronic devices respectively; and sending the second processing datato the first electronic device, so that the first electronic deviceprocesses the second processing data received from the at least twosecond electronic devices to obtain a medical data processing result.

For example, the model data comprises a first sub data set includingmultiple pieces of first sub data; the method comprises: receiving thefirst sub data set from the first electronic device; and based on themultiple pieces of first sub data in the first sub data set, processingthe medical data to obtain the second processing data.

For example, the model data comprises a first sub data set includingmultiple pieces of first sub data, the multiple pieces of first sub dataare divided into a plurality of first sub data groups; the methodcomprises: receiving at least one of the plurality of first sub datagroups from the first electronic device; and processing the medical databased on the first sub data group to obtain the second processing databy the at least two second electronic devices.

For example, at least two groups of the first sub data groups in thefirst sub data set are related to each other; the method comprises:receiving the at least two groups of the first sub data groups relatedto each other from the first electronic device; and processing themedical data based on the at least two groups of the first sub datagroups related to each other to obtain the second processing data by theat least two second electronic devices.

For example, the medical data processing method further comprises:receiving correction data of the second processing data sent from thefirst electronic device; and based on the correction data, obtaining anoptimization parameter of the model data.

For example, the medical data processing method further comprises:receiving updated data of the second processing data sent from the firstelectronic device; and based on the correction data and the updateddata, obtaining the optimization parameter of the model data.

For example, the medical data processing method further comprises:determining whether the model data needs to be changed or not; in a casethat the model data needs to be changed, changing the model data togenerate the first processing data; and sending the first processingdata to the first electronic device, so that the first electronic deviceprocesses the second processing data based on the first processing datato obtain third data.

For example, the model data comprises multiple pieces of first sub data,the first processing data comprises second weights of the multiplepieces of first sub data, the second weights are weights obtained afterthe second electronic devices change initial weights; and the initialweights are initial weights of the multiple pieces of first sub datareceived from the first electronic device.

For example, the medical data comprises a second sub data set includingmultiple pieces of second sub data, the multiple pieces of second subdata in the second sub data set are divided into a plurality of secondsub data groups, the method comprises: receiving at least one of theplurality of second sub data groups from the first electronic device;and based on the model data, processing the at least one of theplurality of second sub data groups to obtain the second processingdata.

For example, each second electronic device comprises a safety processingvirtual area, the safety processing virtual area is an area where thedata is processed safely, the safety processing virtual area is safelyisolated from other areas of the second electronic device, and thesecond electronic device stores the model data and the medical data inthe safety processing virtual area, and processes the medical data basedon the model data in the safety processing virtual area to obtain thesecond processing data.

For example, the second electronic device receives the encrypted modeldata and medical data from the first electronic device, encrypts thesecond processing data and sends the encrypted second processing data tothe first electronic device.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to clearly illustrate the technical solutions of theembodiments of the disclosure, the drawings of the embodiments will bebriefly described in the following; it is obvious that the describeddrawings below only illustrate exemplary embodiments of the disclosure.

FIG. 1 shows an exemplary architecture diagram of a network modelaccording to an embodiment of the present disclosure;

FIG. 2 shows an exemplary flow chart of a medical data acquisitionmethod according to an embodiment of the present disclosure;

FIG. 3 shows an exemplary flow chart of a medical data processing methodaccording to an embodiment of the present disclosure;

FIG. 4 shows an exemplary architecture diagram of a medical data clusterprocessing system according to an embodiment of the present disclosure;and

FIG. 5 shows a flow chart of a medical data cluster processing methodaccording to an embodiment of the present disclosure.

DETAILED DESCRIPTION

In the following, the preferred embodiments of the present disclosurewill be described in detail with reference to the drawings. It should benoted that, in the specification and the drawings, a same label in thedrawings represents to a basically identical step and element, and therepeated explanations of the identical steps and elements will beomitted.

In the following embodiments of the present disclosure, a firstelectronic device and a second electronic device are devices that arecapable of communicating with other devices. Specific forms of the firstelectronic device and the second electronic comprise, but are notlimited to, mobile terminals, personal computers, digital cameras,personal digital assistants, portable computers, etc. The electronicdevices may also be terminal devices or server devices.

FIG. 1 shows an exemplary architecture diagram 100 of a network modelaccording to an embodiment of the present disclosure. Referring to FIG.1, in the network model of an embodiment of the present disclosure, afirst electronic device 110, for example, is a device that initiates anelectronic device cluster and controls the operation of the cluster, andthe first electronic device 110 is referred to as an initiator devicefor convenience. A second electronic device 120, for example, is adevice that responds to a broadcast message of the initiator device andparticipates in the cluster, and the second electronic device 120 isreferred to as a participant device for convenience. In the networkmodel, the network model may comprise only one first electronic device110 and at least two second electronic devices 120. For example, in FIG.1, the network model comprises eight second electronic devices 120.Based on the broadcast message of the initiator device, the initiatordevice and all of the participant devices may form a computer cluster atanytime and anywhere. The initiator device establishes and controls thecluster, the participant devices process medical data according to aninstruction of the initiator device and communicates information withthe initiator device. The structure and function of the initiator deviceand the participant devices are described respectively below.

Firstly, a medical data acquisition method implemented by the initiatordevice is introduced. FIG. 2 shows an exemplary flow chart 200 of amedical data acquisition method according to an embodiment of thepresent disclosure, and the medical data acquisition method of anembodiment of the present disclosure will be described below withreference to FIG. 2.

Referring to FIG. 2, in the step S201: sending model data and medicaldata to at least two second electronic devices, the second electronicdevices being communicatively connected with the first electronicdevice. For example, the model data may be processing model data usedfor processing the medical data, such as a processing algorithm, aprocessing model, a processing tool and the like. The model data mayalso be an analysis model random forest, and the random forest includesa plurality of irrelevant decision trees. The medical data may bemedical data, such as a medical image, medical statistics data and thelike. As described above, the first electronic device may be a clusterinitiator device. The second electronic devices each may be a clusterparticipant device. The first electronic device and the secondelectronic devices may be communicated with each other through thenetwork. In a case that the initiator device initiates a clusterestablishment instruction through the network, after a participantdevice receives the cluster establishment instruction through thenetwork, the participant device has a signal connection with theinitiator device through the network, so as to participate in thecluster established by the initiator device. The cluster comprises theinitiator device and at least two participant devices. Certainly, aperson having ordinary skill in the art can understand that although inthe present disclosure, a medical processing model processes the medicaldata, which is taken as an example to describe the medical dataacquisition method, but the medical data acquisition method may also beapplied to other technical fields. For example, an industrial processingmodel is used to process industrial data, a military processing model isused to process military data, etc.

After the cluster is established, the initiator device may send themodel data and the medical data to at least two participant devices inthe cluster. For example, in a case that a first electronic device needsto process complex medical data, the first electronic device mayinitiate a cluster establishment notification through the network, so asto request other electronic devices to process the medical datatogether. After two second electronic devices participate in thecluster, the first electronic device, as the initiator device, may sendthe medical data that needs to be processed and the processing modeldata of the medical data to respective second electronic devicesrespectively, so as to request respective second electronic devices toprocess the medical data together in parallel, and thus the processingability and efficiency of the medical data is improved.

In the step S202: receiving multiple pieces of second processing datasent from the at least two second electronic devices. The secondprocessing data is processing data obtained by processing the medicaldata based on the model data by the second electronic devices.

In a case that each second electronic device receives the model data andthe medical data from the first electronic device, each secondelectronic device processes the medical data to obtain a correspondingpiece of second processing data, and sends the piece of secondprocessing data to the first electronic device. The first electronicdevice receives multiple pieces of second processing data. For example,a processing module used as the model data, analyzes a medical imageused as the medical data. For example, a disease position in a pair or apiece of the medical images is analyzed and identified to obtain ananalysis result. In order to ensure security of the data, the firstelectronic device may encrypt the model data and the medical data andthen send the encrypted model data and medical data to at least twosecond electronic devices, and the first electronic device receives thecorresponding second processing data encrypted and sent from respectivesecond electronic devices.

In the step S203: comprehensively processing the multiple pieces ofsecond processing data received from at least two second electronicdevices to obtain a medical data processing result.

According to an embodiment of the present disclosure, the model datacomprises a first sub data set including multiple pieces of first subdata. The initiator device respectively sends the first sub data set asa whole to each of the at least two second electronic devices. Eachparticipant device processes the medical data based on all the first subdata in the first sub data set to obtain the corresponding secondprocessing data, and sends the corresponding second processing data tothe initiator device. For example, the processing model data used as themodel data comprises a plurality of processing modules, each processingmodule acts as one first sub data, and all of the processing modulesform the first sub data set. The initiator device may send the pluralityof processing modules included in the processing model data to eachparticipant device, each participant device processes the medical dataaccording to the processing model data formed by the plurality ofprocessing modules and sends the result to the initiator device. In thisway, the initiator device may comprehensively process to obtain themedical data of the third data according to the processing data returnedby each participant device. A comprehensive processing approachmentioned above, for example, may comprise performing statisticalanalysis on all data, determining a maximum, minimum or average value,and so on. For example, analysis results of each participant device onthe disease are combined together to evaluate the diseasecomprehensively.

According to an embodiment of the present disclosure, the model datacomprises a first sub data set including multiple pieces of first subdata. The initiator device may also send a part of the data in the firstsub data set to one of the at least two second electronic devices, andsend the other parts of the data in the first sub data set to the othersof the at least two second electronic devices respectively. For example,the processing model data comprises a plurality of processing modules,each processing module acts as one piece of first sub data, and allprocessing modules form the first sub data set. Also for example, themodel data is an analysis model random forest, with a plurality ofirrelevant decision trees of the analysis model random forest beingdivided into a plurality of groups, and the plurality of groups isassigned to different participant devices. The initiator device maydivide the plurality of processing modules included in the processingmodel data into multiple parts according to the number of theparticipant devices, and sends different parts to different initiatordevices respectively. Each participant device processes the medical dataaccording to the received one or more groups of processing modules ordecision trees, and sends the result to the initiator device. In thisway, in a case that the medical data is complex and the data processingmodel is also very large, the initiator may instruct each participantdevice to process the medical data according to a part of processingmodules, and combine the processing results returned by each participantdevice to obtain a final medical data processing result. For example,the initiator device needs to analyze the medical data in differentways, and therefore the data processing model comprises a first analysismodule, a second analysis module, . . . , and an Nth analysis module. Inorder to improve computing efficiency, the initiator device may dividethe first analysis module, the second analysis module, . . . , and theNth analysis module into several parts, and assign the several parts toa plurality of participant devices. The initiator device receives andcombines the analysis results of respective participant devices, so asto improve the processing efficiency of the complex medical data.

According to an embodiment of the present disclosure, when the initiatordevice assigns the processing modules of the data processing model, theprocessing performance parameters of the participant device may beconsidered to set allocation rules. For example, the processingperformance parameters can be obtained from the participant device, andbased on the processing performance parameters of the participantdevice, the processing module(s) of the data processing model sent tothe participant device is determined. For example, the initiator deviceobtains information of the participant devices, such as a processingspeed of a processor, a storage capacity of a memory and so on, from theparticipant devices. The initiator device sends a processing module witha large amount of computation to a participant device with a highprocessing speed, and sends a processing module that generates a largeamount of data to a participant device with a large storage capacity. Bybalancing the performance parameters of the participant devices, theadvantages of each participant device may be utilized, so as to furtherimprove the parallel processing capability of the cluster.

According to an embodiment of the present disclosure, in a case thatpart of the first sub data in the first sub data set is related to eachother, for example, the first sub data and the second sub data arerelated to each other. The initiator device may send the first sub dataand the second sub data which are related to each other to one secondelectronic device based on the relationship between the first sub data.For example, in a case that the data processing model comprises thefirst analysis module, the second analysis module and other processingmodules; if the second analysis module is an analysis module based on ananalysis result of the first analysis module, then the initiator devicemay send the first analysis module and the second analysis module to oneparticipant device, and receive corresponding second processing dataanalyzed based on the first analysis module and the second analysismodule from the participant device, so that a data transmission timebetween the initiator device and the plurality of participant devices issaved.

Certainly, the present disclosure is not limited thereto. According toanother embodiment of the present disclosure, in a case that the firstsub data and the second sub data in the first sub data set are relatedto each other, the initiator device may also send part of the first subdata related to each other to one second electronic device, and send theother part of the first sub data to other second electronic devices. Forexample, in a case that the data processing model comprises the firstanalysis module, the second analysis module and other processingmodules; if the second analysis module is an analysis module based on ananalysis result of the first analysis module, then the initiator devicemay also send the first analysis module to a participant device A andsend the second analysis module to a participant device B. Under thiscircumstance, the participant device B may process the medical data byadopting other ways. For example, based on a first analysis result inprevious historical data, the second analysis module may be used toanalyze the first analysis result in the historical data to obtain anapproximate analysis result. Or, based on the first analysis module inthe historical data and the received second analysis module, the medicaldata is processed.

According to an embodiment of the present disclosure, the initiatordevice may also use the participant device to optimize the processingdata model. After the participant device sends the processed secondprocessing data to the initiator device, the initiator device maycorrect the second processing data and send correction data, e.g., thesecond processing data after being corrected, to the participant deviceagain. The participant device obtains optimization parameters of theprocessing module(s) in the processing model data based on the receivedcorrection data.

In addition, according to an embodiment of the present disclosure, afterthe participant device sends the processed second processing data to theinitiator device, in addition to correcting the second processing data,the initiator device may also update the data, such as adding new data,and send the corrected and updated data to the participant device again.The participant device obtains the optimization parameters of theprocessing module(s) in the processing model data based on the correctedand updated data being received.

In order to improve the parallelization of data processing, theinitiator device may assign the processing modules with a processingsequence relationship in the processing data model to differentparticipant devices, so that all participant devices may process themedical data in parallel compulsorily. In order to ensure the accuracyof the data, the initiator device may correct the processing resultcalculated by each participant device. The way in which the initiatordevice corrects the data, for example, may be a way in which theprocessing result is corrected by using known data, threshold data or anexpected value.

According to an embodiment of the present disclosure, the initiatordevice may determine an initial weight of each first sub data in thefirst sub data set. When sending first sub data in the first sub dataset to the participant device, the initial weight(s) of thecorresponding first sub data is sent to the participant devicesimultaneously. In this way, when the second processing data receivedfrom at least two participant devices are combined, the initiator devicemay comprehensively process the multiple pieces of second processingdata received from the at least two second electronic devices based onthe initial weight(s) of the first sub data to obtain the third data.For example, in a case that the data processing model comprises aplurality of processing algorithms and each processing algorithm has adifferent initial weight based on the accuracy or effectiveness of thealgorithm, the initiator device may obtain the final medical analysisresult by multiplying the processing results received from theparticipant device by the corresponding weights.

According to an embodiment of the present disclosure, the participantdevice may process the processing model data sent by the initiatordevice, such as changing or correcting the processing model data. Forexample, after receiving the data processing model from the initiatordevice, the participant device determines whether the data processingmodel needs to be changed or not; in a case that the data processingmodel needs to be changed, the participant device changes the dataprocessing model correspondingly. For example, in a case that the dataprocessing model received from the initiator device comprises aplurality of processing modules and each processing module has aninitial weight, under this circumstance, the participant devicedetermines whether the initial weight of each received processing moduleneeds to be changed or not according to the historical data thereof. Ina case that the initial weight needs to be changed, the initial weightis modified to a second weight, and the second weight is returned to theinitiator device subsequently. For example, in a case that thehistorical data shows that the initial weight received does not matchwith the corresponding processing module, it is determined that theinitial weight needs to be changed, and then the participant device maychange the initial weight correspondingly. The initiator devicecomprehensively evaluates the second processing data according to thesecond weight(s) to obtain the final medical analysis result. Certainly,the participant device changing the weight is just an example, and aperson having ordinary skill in the art can understand that theparticipant device may also change other properties of the dataprocessing model and the medical data received from the initiator deviceaccording to requirements. For example, the other properties comprise aversion and content of the data processing model, a format and contentof the medical data and so on.

According to an embodiment of the present disclosure, the medical datamay comprise a second sub data set including multiple pieces of secondsub data. The initiator device may send a first part of the second subdata in the second sub data set to one of the at least two secondelectronic devices, and send a second part of the second sub data in thesecond sub data set to the other(s) of the at least two secondelectronic devices respectively. For example, the medical data is amedical image, and the initiator device needs to recognize the medicalimage to determine whether the medical image has a lesion area. In orderto improve the recognition efficiency, the initiator device may dividethe medical image into a plurality of blocks, send one or more of theplurality of blocks to one participant device, and send the other blocksto other participant devices. Each participant device recognizes itsreceived image block(s), and sends a recognition result to the initiatordevice. After the initiator device receives a corresponding recognitionresult of the recognized image block(s) sent from each participantdevice, the initiator device combines the recognition results to obtainthe desired medical data. For example, after combining the recognitionresults, it can be determined whether the whole medical image has thelesion area, where the lesion area is located and so on. For example,each participant device scores each pixel in the image that is processedby itself to determine whether the pixel is the lesion area or not;after the initiator device receives the processing result, the initiatordevice may add the score of each participant device together todetermine whether a sum of the scores exceeds to a preset threshold ornot, so as to determine a location and size of the lesion area.

According to an example of the present disclosure, in order to ensurethe safety of the medical data and protect the privacy of the patients,a safety processing virtual area may be disposed in part or all of theparticipant devices. The safety processing virtual area is safelyisolated from the other areas of the participant device. The participantdevice stores the data processing model and the medical data receivedfrom the initiator device in the safety processing virtual area. And,the participant device processes the medical data based on the modeldata in the safety processing virtual area to obtain the secondprocessing data. For example, the initiator device is provided with asandbox mechanism; except that the initiator device may control thesafety processing virtual area of the participant device and theprocessing operation in the safety processing virtual area, the otherdevices or components may not access and control the safety processingvirtual area of the participant device. For example, even users of theparticipant device itself can not control the data processing in thesafety processing virtual area, so as to ensure the closure of the wholeprocess and the privacy of patient data. In addition, even if the datareceived from the initiator device contains a virus program, because alldata received from the initiator device is stored in the safetyprocessing virtual area and is processed isolatedly, other devices andcomponents of the participant device are not affected by the virusprogram, and the safety of the participant device is ensured.

Through establishing a computer cluster, the present disclosure providesthe feasibility of computing the complex medical data in parallel byexisting general devices. Because the cluster may be established atanytime and anywhere, the flexibility of data processing is improved.Moreover, because the data is processed in the safety processing virtualarea, so as to effectively protect the privacy of the patient data.

According to an embodiment of the present disclosure, a medical dataprocessing method is provided, and the medical data processing method isapplied to a participant device in a cluster, namely the medical dataprocessing method is applied to a process that a cluster participantresponses to an instruction of a cluster initiator to process themedical data. In the above description of operations performed by theinitiator device in the cluster, operations performed by the clusterparticipant device has been described in details, and for the simplicityof the specification, a brief introduction will be given in thefollowing, and the details may be referred to in the above embodiments.

FIG. 3 shows an exemplary flow chart 300 of a medical data processingmethod according to an embodiment of the present disclosure. The medicaldata processing method is applied to at least two second electronicdevices respectively. Referring to FIG. 3, in the step S301: receivingthe model data and the medical data from the first electronic device,the second electronic device having signal connection with the firstelectronic device. In the step S302: processing, by the at least twosecond electronic devices respectively, the medical data based on themodel data to obtain the second processing data. In the step S303:sending the second processing data to the first electronic device, sothat the first electronic device comprehensively processes the secondprocessing data received from the at least two second electronic devicesto obtain a medical data processing result.

According to an example of the present disclosure, the model datacomprises a first sub data set including multiple pieces of first subdata. The medical data processing method comprises: receiving the firstsub data set from the first electronic device; and based on the multiplepieces of first sub data in the first sub data set, processing themedical data to obtain the second processing data.

According to an example of the present disclosure, the model datacomprises a first sub data set including multiple pieces of first subdata, and the multiple pieces of first sub data are divided into aplurality of groups. The medical data processing method comprises:receiving one or more groups of the first sub data; and processing themedical data based on the one or more groups of the first sub data toobtain the second processing data.

According to an example of the present disclosure, at least two groupsof the first sub data in the first sub data set are related to eachother. The medical data processing method comprises: receiving twogroups of the first sub data that are related to each other from thefirst electronic device; and processing the medical data based on thetwo groups of the first sub data related to each other to obtain thesecond processing data.

According to an example of the present disclosure, the medical dataprocessing method comprises: receiving correction data of the secondprocessing data sent from the first electronic device; and based on thecorrection data, obtaining an optimization parameter of the model data.

According to an example of the present disclosure, the medical dataprocessing method comprises: receiving updated data of the secondprocessing data sent from the first electronic device; and based on thecorrection data and the updated data, obtaining the optimizationparameter of the model data.

According to an example of the present disclosure, the medical dataprocessing method comprises: determining whether the model data needs tobe changed or not; in a case that the model data needs to be changed,changing the model data to generate first processing data; and sendingthe first processing data to the first electronic device, so that thefirst electronic device processes the second processing data based onthe first processing data to obtain the third data.

According to an example of the present disclosure, the model datacomprises multiple pieces of first sub data, the first processing datacomprises a second weight for each piece of the first sub data, and thesecond weight is a weight obtained after changing an initial weight bythe second electronic device. The initial weight is the initial weightof a corresponding piece of the first sub data received from the firstelectronic device.

According to an example of the present disclosure, the medical datacomprises a second sub data set including multiple pieces of second subdata, the multiple pieces of second sub data in the second sub data setare divided into at least two groups, and the medical data processingmethod comprises: receiving one or more groups of the second sub datafrom the first electronic device; and processing the one or more groupsof the second sub data based on the model data to obtain the secondprocessing data.

According to an example of the present disclosure, the second electronicdevice comprises a safety processing virtual area, the safety processingvirtual area is safely isolated from the other areas of the secondelectronic device, and the second electronic device stores the modeldata and the medical data in the safety processing virtual area, andprocesses the medical data based on the model data in the safetyprocessing virtual area to obtain the second processing data.

According to an example of the present disclosure, the second electronicdevice receives the encrypted model data and medical data from the firstelectronic device, encrypts the processed second processing data, andsends the encrypted second processing data to the first electronicdevice.

Embodiments of the present disclosure process the medical data inparallel, so as to improve the processing speed and the processingcapacity. Moreover, because the cluster may be established at any time,so that the parallel computation of the complex medical data is possiblewithout a large medical data processing device.

According to an embodiment of the present disclosure, a medical datacluster processing system is provided. FIG. 4 shows an exemplaryarchitecture diagram of a medical data cluster processing systemaccording to an embodiment of the present disclosure. Referring to FIG.4, the medical data cluster processing system 400 comprises a clusterinitiator device 410 and cluster participant devices 420 as mentionedabove. For the simplicity of the specification, a brief introductionwill be given in the following, and the details may be referred to theabove embodiments.

The cluster initiator device sends model data and medical data to atleast two cluster participant devices; the cluster participant devicesreceive the model data and the medical data from the cluster initiatordevice, process the medical data based on the model data to obtainsecond processing data, and send the second processing data to thecluster initiator device; and the cluster initiator device receives thesecond processing data sent from the at least two cluster participantdevices, and comprehensively processes the second processing data toobtain third data. The third data comprises medical data.

According to an example of the present disclosure, each clusterparticipant device comprises a safety processing virtual area, and thesafety processing virtual area is safely isolated from the other areasof the cluster participant device; the cluster participant deviceprocesses the medical data based on the model data in the safetyprocessing virtual area, so as to obtain the second processing data.

According to an example of the present disclosure, the cluster initiatordevice accesses the safety processing virtual area of the clusterparticipant device and controls the processing in the safety processingvirtual area, and the cluster participant device is not capable ofaccessing the safety processing virtual area.

Embodiments of the present disclosure provide feasibility of computingthe complex medical data through establishing the computer cluster.Moreover, because the data is processed in the safety processing virtualareas, so as to effectively protect the privacy of the patient data.

According to an embodiment of the present disclosure, a medical datacluster processing method is provided. The medical data clusterprocessing method corresponds to the cluster processing system in theembodiments mentioned above, the cluster initiator device and thecluster participant devices in the medical data cluster processingmethod are similar to the cluster initiator device and the clusterparticipant devices in the embodiments described above. For thesimplicity of the specification, a brief introduction will be given inthe following, and the details may be referred to the description in theabove embodiments.

In the medical data cluster processing method, the cluster comprises acluster initiator device and at least two cluster participant devices,and the cluster initiator device has signal connection with the at leasttwo cluster participant devices. FIG. 5 shows a flow chart 500 of amedical data cluster processing method according to an embodiment of thepresent disclosure. Referring to FIG. 5, in the step S501: the clusterinitiator device sending model data and medical data to at least twocluster participant devices respectively. In the step 502: the clusterparticipant devices receiving the model data and the medical data fromthe cluster initiator device. In the step S503: the cluster participantdevices processing the medical data based on the model data received toobtain second processing data, and sending the second processing data tothe cluster initiator device. In the step S504: the cluster initiatordevice receiving the second processing data sent from the at least twocluster participant devices, and comprehensively processing the secondprocessing data to obtain a medical data processing result.

According to an example of the present disclosure, the cluster initiatordevice divides the model data into multiple pieces of first sub data,the cluster initiator device sends a first sub data set including themultiple pieces of first sub data as a whole to each of the at least twocluster participant devices; each cluster participant device receivesthe first sub data set from the cluster initiator device; and eachcluster participant device processes the medical data based on the firstsub data set received to obtain a corresponding piece of secondprocessing data, and sends the corresponding piece of second processingdata to the cluster initiator device.

According to an example of the present disclosure, the cluster initiatordevice divides the model data into multiple pieces of first sub data,and divides the multiple pieces of first sub data into a plurality ofgroups; the cluster initiator device sends one or more groups of thefirst sub data to one of the at least two cluster participant devices,and sends other groups of the first sub data to the others of the atleast two cluster participant devices; each cluster participant devicereceives one or more groups of the first sub data from the clusterinitiator device; each cluster participant device processes the medicaldata based on the one or more groups of the first sub data received toobtain a corresponding piece of the second processing data, and sendsthe corresponding piece of the second processing data to the clusterinitiator device.

According to an example of the present disclosure, the cluster initiatordevice divides the medical data into multiple pieces of second sub data,the cluster initiator device divides the multiple pieces of second subdata into at least two groups, the cluster initiator device assigns oneor more groups of the second sub data to a second electronic device andassigns the other groups of the second sub data to the other secondelectronic devices; each cluster participant device processes one ormore groups of the second sub data received based on the model data toobtain the corresponding piece of the second processing data, and sendsthe corresponding piece of the second processing data to the clusterinitiator device.

Embodiments of the present disclosure adopt the computer cluster toprocess the medical data, so that the complex medical data may becalculated and processed rapidly.

A person having ordinary skill in the art can realized that, incombination with the embodiments of the present disclosure described inthe specification, units and algorithm steps of each example can beimplemented by electronic hardware, computer software or the combinationof the two. In addition, each software module may be stored in computerstorage media having any form. In order to clearly describeinterchangeability of hardware and software, in the above description,components and steps of each example have been described in generalaccording to functions. The functions are implemented in hardware or insoftware, which depends on the particular application and designconstraints of the technical solution. A person having ordinary skill inthe art may use different methods to implement the described functionfor each particular application, but the implementation of the describedfunction should not be understood as being outside the scope of thedisclosure.

A person having ordinary skill in the art should understand that variousmodification, combinations, partial combinations and substitutions maybe made to the present disclosure depending on design requirement andother factors, provided that all of the modification, combinations,partial combinations and substitutions fall within the scope of theclaims and equivalents thereof.

The application claims priority to the Chinese patent application No.201610810601.8, filed Sep. 8, 2016, the entire disclosure of which isincorporated herein by reference as part of the present application.

What is claimed is:
 1. A medical data cluster processing system,comprising: a cluster initiator device and cluster participant deviceshaving signal connection with the cluster initiator, wherein: thecluster initiator device initiates, in a case where the clusterinitiator device processes a medical image, a cluster establishmentnotification to request at least two cluster participant devices toparticipate a cluster to process the medical image together with thecluster initiator device, and sends model data and the medical image toeach of the at least two cluster participant devices in a case where theat least two cluster participant devices participate the cluster; eachof the at least two cluster participant devices participates the clusterin response to the cluster establishment notification, receives themodel data and the medical image from the cluster initiator device,performs an image recognition operation on the medical image based onthe model data to obtain a recognition result, and sends the recognitionresult to the cluster initiator device; the cluster initiator devicereceives the recognition results sent from the at least two clusterparticipant devices, and comprehensively processes the recognitionresults to determine a location and a size of a lesion area in themedical image, the image recognition operation comprises determining ascore of each pixel in an area of the medical image which is processedby the cluster participant device and using the score as the recognitionresult; and the cluster initiator device is further configured toreceive the scores sent from the at least two cluster participantdevices and add the scores to determine whether a sum of the scoresexceeds a preset threshold to determine the location and the size of thelesion area in the medical image.
 2. The medical data cluster processingsystem according to claim 1, wherein the cluster participant deviceseach comprise a safety processing virtual area, the safety processingvirtual area is safely isolated from other areas of the clusterparticipant devices; each of the cluster participant devices performsthe image recognition operation on the medical image based on the modeldata in the safety processing virtual area, so as to obtain therecognition result; and the cluster initiator device accesses the safetyprocessing virtual area of each cluster participant device and controlsthe processing in the safety processing virtual area, and the clusterparticipant devices do not have access to the safety processing virtualarea.
 3. The medical data cluster processing system according to claim1, wherein: the cluster initiator device divides the model data intomultiple pieces of first sub data; the cluster initiator device sends afirst sub data set including the multiple pieces of first sub data as awhole to each of the at least two cluster participant devicesrespectively; each cluster participant device receives the first subdata set from the cluster initiator device; and each cluster participantdevice performs the image recognition operation on the medical imagebased on the first sub data set received to obtain a correspondingrecognition result, and sends the corresponding recognition result tothe cluster initiator device.
 4. The medical data cluster processingsystem according to claim 1, wherein: the cluster initiator devicedivides the model data into multiple pieces of first sub data, anddivides the multiple pieces of first sub data into multiple groups; thecluster initiator device sends one or more groups of the first sub datato one of the at least two cluster participant devices, and sends othergroups of the first sub data to others of the at least two clusterparticipant devices; each cluster participant device receives one ormore corresponding groups of the first sub data from the clusterinitiator device; and each cluster participant device performs the imagerecognition operation on the medical image based on the one or moregroups of the first sub data received to obtain a correspondingrecognition result, and sends the corresponding recognition result tothe cluster initiator device.
 5. The medical data cluster processingsystem according to claim 1, wherein: the cluster initiator devicedivides the medical image into multiple blocks; the cluster initiatordevice divides the multiple blocks into at least two groups, assigns oneor more groups of the blocks to a second electronic device and assignsremaining groups of the blocks to other second electronic devices; andeach cluster participant device performs the image recognition operationon the received groups of the blocks based on the model data to obtainthe corresponding recognition result, and sends the correspondingrecognition result to the cluster initiator device.
 6. A medical dataprocessing method, applied in a first electronic device, comprising:initiating, in a case where the first electronic device processes amedical image, a cluster establishment notification to request at leasttwo second electronic devices to participate a cluster to process themedical image together with the first electronic device, the secondelectronic devices being in signal connection with the first electronicdevice; sending model data and the medical image to the at least twosecond electronic devices, in a case where the second electronic devicesparticipate the cluster; receiving multiple recognition results sentfrom the at least two second electronic devices, wherein the multiplerecognition results are obtained by performing an image recognitionoperation on the medical image based on the model data by the secondelectronic devices, the image recognition operation comprisesdetermining a score of each pixel in an area of the medical image whichis processed by the cluster participant device and using the score asthe recognition result; and processing the multiple recognition resultsreceived from the at least two second electronic devices to determine alocation and a size of a lesion area in the medical image, wherein theprocessing the multiple recognition results received from the at leasttwo second electronic devices to determine the location and the size ofthe lesion area in the medical image comprises adding the scores sentfrom the at least two cluster participant devices to determine whether asum of the scores exceeds a preset threshold to determine the locationand the size of the lesion area in the medical image.
 7. The medicaldata processing method according to claim 6, wherein, the model datacomprises a first sub data set including multiple pieces of first subdata, and the first sub data set as a whole is sent to each of the atleast two second electronic devices; or, the multiple pieces of firstsub data in the first sub data set are grouped to obtain a plurality offirst sub data groups, at least one of the first sub data groups is sentto one of the at least two second electronic devices, and other firstsub data groups are sent to others of the at least two second electronicdevices.
 8. The medical data processing method according to claim 7,comprising: obtaining processing performance parameters of the at leasttwo second electronic devices; and sending the first sub data groups tothe at least two second electronic devices based on the processingperformance parameters of the at least two second electronic devices. 9.The medical data processing method according to claim 7, comprising:determining an initial weight of each piece of first sub data; when apiece of first sub data in the first sub data set is sent to a secondelectronic device, sending an initial weight of the piece of first subdata to the second electronic device simultaneously; and based oninitial weights of the multiple pieces of first sub data, processing therecognition results received from the at least two second electronicdevices to determine the location and the size of the lesion area in themedical image.
 10. The medical data processing method according to claim7, wherein at least two pieces of first sub data in the first sub dataset are related to each other, the at least two pieces of first sub datarelated to each other are sent to one second electronic device, or theat least two pieces of first sub data related to each other arerespectively sent to different second electronic devices.
 11. Themedical data processing method according to claim 10, furthercomprising: receiving the recognition results sent from the at least twosecond electronic devices respectively; and correcting the recognitionresults to determine the location and the size of the lesion area in themedical image.
 12. The medical data processing method according to claim6, comprising: receiving first processing data sent by the at least twosecond electronic devices, wherein the first processing data includesresults obtained after the second electronic devices process the modeldata; and based on the first processing data, processing the multiplerecognition results sent by the at least two second electronic devicesto determine the location and the size of the lesion area in the medicalimage, wherein the model data comprises multiple pieces of first subdata; wherein the first processing data comprises second weights of themultiple pieces of first sub data, the second weights are weights afterprocessing the initial weights by the second electronic devices; whereinthe initial weights are the initial weights of the multiple pieces offirst sub data sent to the second electronic devices; and wherein therecognition result is processed based on the second weights of themultiple pieces of first sub data to determine the location and the sizeof the lesion area in the medical image.
 13. The medical data processingmethod according to claim 6, wherein the medical image comprises a blockset including multiple blocks of the medical image; the multiple blocksof the medical image in the block set are divided into at least twogroups to obtain a plurality of block groups; at least one of the blockgroups is sent to one of the at least two second electronic devices; andother block groups are sent to others of the at least two secondelectronic devices.
 14. A medical data processing method, applied in atleast two second electronic devices, comprising: receiving a clusterestablishment notification from a first electronic device requesting thesecond electronic devices to participate a cluster to process a medicalimage together with the first electronic device, and participating thecluster in response to the cluster establishment notification, thesecond electronic devices being in signal connection with the firstelectronic device; receiving model data and the medical image from afirst electronic device; based on the model data, performing an imagerecognition operation on the medical image to obtain a recognitionresult by each of the at least two second electronic devices, whereinthe image recognition operation comprises determining a score of eachpixel in an area of the medical image which is processed by the clusterparticipant device and using the score as the recognition result; andsending the scores to the first electronic device, so that the firstelectronic device-adds the scores sent from the at least two clusterparticipant devices to determine whether a sum of the scores exceeds apreset threshold to determine the location and the size of the lesionarea in the medical image.
 15. The medical data processing methodaccording to claim 14, wherein the model data comprises a first sub dataset including multiple pieces of first sub data, and the methodcomprises: receiving the first sub data set from the first electronicdevice; and based on the multiple pieces of first sub data in the firstsub data set, processing the medical image to obtain the recognitionresult; or the multiple pieces of first sub data are divided into aplurality of first sub data groups, and the method comprises: receivingat least one of the plurality of first sub data groups from the firstelectronic device; and performing the image recognition operation on themedical image based on the first sub data group to obtain therecognition results by the at least two second electronic devices. 16.The medical data processing method according to claim 15, wherein atleast two groups of the first sub data groups in the first sub data setare related to each other; the method comprises: receiving the at leasttwo groups of the first sub data groups related to each other from thefirst electronic device; and performing the image recognition operationon the medical image based on the at least two groups of the first subdata groups related to each other to obtain the recognition results bythe at least two second electronic devices.
 17. The medical dataprocessing method according to claim 14, further comprising: receivingcorrection data of the recognition results sent from the firstelectronic device; receiving updated data of the recognition resultssent from the first electronic device; and based on the correction dataand the updated data, obtaining an optimization parameter of the modeldata.
 18. The medical data processing method according to claim 14,further comprising: determining whether the model data needs to bechanged or not; in a case that the model data needs to be changed,changing the model data to generate the first processing data; andsending the first processing data to the first electronic device, sothat the first electronic device processes the recognition results basedon the first processing data to determine the location and the size ofthe lesion area in the medical image, wherein the model data comprisesmultiple pieces of first sub data, and wherein the first processing datacomprises second weights of the multiple pieces of first sub data, thesecond weights are weights obtained after the second electronic deviceschange initial weights, and the initial weights are initial weights ofthe multiple pieces of first sub data received from the first electronicdevice.
 19. The medical data processing method according to claim 14,wherein the medical image comprises a block set including multipleblocks of the medical image, the multiple blocks of the medical image inthe second sub data set are divided into a plurality of block groups,the method comprises: receiving at least one of the plurality of blockgroups from the first electronic device; and based on the model data,performing the image recognition operation on the at least one of theplurality of block groups to obtain the recognition result, wherein eachsecond electronic device comprises a safety processing virtual area, thesafety processing virtual area is an area where the data is processedsafely, the safety processing virtual area is safely isolated from otherareas of the second electronic device, and the second electronic devicestores the model data and the medical image in the safety processingvirtual area, and performs the image recognition operation on the atleast one of the plurality of block groups based on the model data inthe safety processing virtual area to obtain the recognition result.